Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

Chang, Ray-I; Lee, Chia‐Yun; Hung, Yao-Min

doi:10.3390/app11219945

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…These records are extracted later or in real time to apply PdM. Examples of this case are found on the one hand in industrial processes (Bampoula et al, 2021; Bekar et al, 2020; Chang et al, 2021; Kim et al, 2021; Kolokas et al, 2020; Lepenioti et al, 2020; Li et al, 2017; Morariu et al, 2020; Rafique et al, 2018; Ruiz‐Sarmiento et al, 2020; Susto et al, 2015; Uhlmann et al, 2018; Zschech et al, 2019), in production lines (Ayvaz & Alpay, 2021; Azab et al, 2021; Cerquitelli et al, 2021; Fathi et al, 2021; Giordano et al, 2021; Liu et al, 2021), in power plants (de Carvalho Chrysostomo et al, 2020; Khodabakhsh et al, 2018; Sun et al, 2021; Zhang, Liu, et al, 2018), in wind turbines (Chen, Hsu, et al, 2021; Leahy et al, 2018; Santolamazza et al, 2021), in ventilation systems (Fernandes et al, 2020), cryogenic pumps (Crespo Márquez et al, 2020), heat meters (Pałasz & Przysowa, 2019), press machines (Serradilla et al, 2021), or water treatment plants (Srivastava et al, 2018). Another common scenario for PdM is related to transportation: different types of land vehicles (Chen et al, 2020; Patil et al, 2021; Prytz et al, 2015; Shafi et al, 2018), aircrafts (Baptista et al, 2021; Basora et al, 2021; Ning et al, 2021; Savitha et al, 2020; Yang et al, 2017), and naval ships (Berghout et al, 2021; Fernández‐Barrero et al, 2021; Gribbestad et al, 2021) have been monitored through their electronic control units.…”

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

“…In Schlagenhauf and Burghardt (2021), LiR is the last step in a decision support system that estimates the evolution of a damage area in the monitored surface. In Chang et al (2021), different regression methods are compared to analyze complex manufacturing data and predict the future behavior of the monitored machine. Specifically, LiR, Least Absolute Shrinkage Selector Operator and Ridge and Elastic Net regressors are compared.…”

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

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Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under: Application Areas > Industry Specific Applications Technologies > Internet of Things

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Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

Section: Data Mining In Predictive Maintenancementioning

confidence: 99%

Data mining in predictive maintenance systems: A taxonomy and systematic review

Esteban

Zafra

Ventura

2022

WIREs Data Min & Knowl

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“…Still, IoT implementation in the textile industry will be more difficult than in other fields (Manglani et al, 2019). Five articles were published on the use of IoT in various industries (Chang et al, 2021; Chen, 2019; Ghoreishi & Happonen, 2022; Manglani et al, 2019; Rath et al, 2021). Among these publications, one study (Chen, 2019) examined how SMEs utilized innovative technologies to combine manufacturing supply networks and participate in global supply chains in the fourth industrial revolution, using four textile SMEs in Taiwan as a case study.…”

Section: Content Analysismentioning

confidence: 99%

“…The case of fabric and textile industry: The emerging role of digitalization, internet-of-things and Industry 4.0 for circularityGhoreishi and Happonen (2022) Value creation by SMEs participating in global value chains under Industry 4.0 trend: Case study of textile industry in TaiwanChen (2019) Digitizing a distributed textile production process using industrial internet of things: A use-caseRath et al (2021) Application of the internet of things in the textile industryManglani et al (2019) Cloud-based analytics module for predictive maintenance of the textile manufacturing processChang et al (2021) …”

mentioning

confidence: 99%

Industry 4.0 in Textile and Apparel Industry: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends

Deepthi

Bansal

2022

Vision: The Journal of Business Perspective

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The manufacturing industry is poised to undergo a paradigm shift with the advent of the Fourth Industrial Revolution, popularly known as ‘Industry 4.0’, which will integrate the physical and digital worlds. As a widely acknowledged phrase among research institutions and universities, the ‘Industry 4.0’ paradigm has attracted significant interest from the academic, business and scientific communities. Even though the concept is not new and has been at the forefront of scholarly research for many years with many interpretations, the ‘Industry 4.0’ concept has just recently been introduced. It is widely accepted not only in the research field but also in the manufacturing ecosystems. However, there is a need to comprehend industry-specific research advancements, trends and gaps due to the diverse applications of different technologies. This article systematically reviews and comprehensively evaluates the research on different Industry 4.0 efforts and their applicability in the textile and manufacturing industries. This article aims to map the current Industry 4.0 literature in the textile and apparel industry to analyse and categorize existing research and identify research gaps. We utilized the PRISMA framework to conduct a systematic review of the literature and 34 research publications on Industry 4.0 in the textile and apparel industry were located using a well-organized keyword search of the Web of Science and Scopus databases. The findings indicate that, of all Industry 4.0 technologies, the internet of things (IoT) and RFID applications are the most extensively investigated in the textile and manufacturing industries. Despite the extensive applications of additive manufacturing (AM)/3D printing and augmented reality (AR), research in both fields is still in its infancy. The study revealed that Germany is the country that has published the most literature on Industry 4.0 projects in the textile and apparel industries. We urge that future academics focus on determining the relevance of Industry 4.0 initiatives to small and medium-sized enterprises (SMEs) as the textile and apparel industry is dominated by SMEs in many developing countries.

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“…Machine learning techniques have made it possible to optimize the time and accuracy of operations in industrial, business and academic environments, among others. Bibliometric analysis evidences the growing interest in auto-mathematical learning in the textile industry [2] [3] [4][5][6][7][8][9][10][11][12], algorithms that study and detect fibers in images [13], among others. This research is based on the bibliometric analysis methodology proposed by [1] for the analysis of bibliographic interest in the proposed topic of study.…”

Section: Introductionmentioning

confidence: 99%

Textile and machine learning: A Bibliometric analysis

Umaquinga

Toro

COLLAGUAZO

2022

IDEAS

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Abstract. In the present research, a bibliometric analysis of scientific publications in the area of textile sciences from the area of machine learning is carried out. The search equation identifies as keywords (i) textile and (ii) machine learning from the scopus database, obtaining as initial result 308 publications, being 281 the final publications to be studied from 1991 to 2022. The bibliometric analysis was carried out using bibliometrix and VOS Viewer, showing the growing interest of the scientific community and authors in this area of research.

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Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

Cited by 14 publications

References 34 publications

Data mining in predictive maintenance systems: A taxonomy and systematic review

Data mining in predictive maintenance systems: A taxonomy and systematic review

Industry 4.0 in Textile and Apparel Industry: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends

Textile and machine learning: A Bibliometric analysis

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